#remotes::install_github("braverock/FactorAnalytics",  build_vignettes = TRUE, force = TRUE)
pacman::p_load(tidyverse,tidyquant,FFdownload,FactorAnalytics,PerformanceAnalytics,PortfolioAnalytics,nloptr)

Please remember to put your assignment solutions in rmd format using many chunks and putting readable text in between, similar to my examples given in Research Methods and Assignment 1!

For all exercises: Please use the Assignment-Forum to post your questions, I will try my best to help you along!

Exercise 1: Analysing the CAPM

In this exercise we want to estimate the CAPM. Please read carefully through the two documents provided (right hand side: files). Then we start to collect the necessary data:

  1. From Datastream get the last 10 years of data from the 100 stocks of the S&P100 using the list LS&P100I (S&P 100): total return index (RI) and market cap (MV)
library(readxl)
sp100_daily_RI <- read_excel("sp100 daily RI_2.xlsx")
head(sp100_daily_RI, n=10)
library(readxl)
sp100_monthly_MV <- read_excel("sp100 monthly MV.xlsx")
head(sp100_monthly_MV, n=10)
  1. Further import the Fama-French-Factors from Kenneth Frenchs homepage (monthly, e.g. using FFdownload). From both datasets we select data for the last (available) 60 months, calculate returns (simple percentage) for the US-Stocks and eliminate those stocks that have NAs for this period.
 tempf <- tempfile(fileext = ".RData"); tempd <- tempdir(); temptxt <- tempfile(fileext = ".txt")
 inputlist <- c("F-F_Research_Data_Factors","F-F_Market_Beta")
 FFdownload(exclude_daily=TRUE,tempdir=tempd,download=TRUE,download_only=FALSE,inputlist=inputlist)
Step 1: getting list of all the csv-zip-files!

Step 2: Downloading 2 zip-files

trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/ME_Breakpoints_CSV.zip'
Step 3: Start processing 2 csv-files

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 tempf2 <- tempfile(fileext = ".RData"); tempd2 <- tempdir() 
 FFdownload(output_file = tempf2,tempdir = tempd2,exclude_daily = TRUE, download = TRUE, download_only=FALSE, listsave=temptxt)
Step 1: getting list of all the csv-zip-files!

Step 2: Downloading 193 zip-files

trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_Factors_weekly_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_5_Factors_2x3_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_ME_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_BE-ME_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_OP_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_INV_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_2x3_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_2x3_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_2x3_weekly_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_5x5_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/100_Portfolios_10x10_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/100_Portfolios_10x10_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_OP_2x3_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_OP_2x3_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_OP_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_OP_5x5_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/100_Portfolios_ME_OP_10x10_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/100_Portfolios_10x10_ME_OP_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_INV_2x3_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_INV_2x3_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_INV_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_INV_5x5_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/100_Portfolios_ME_INV_10x10_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/100_Portfolios_10x10_ME_INV_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_BEME_OP_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_BEME_OP_5x5_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_BEME_INV_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_BEME_INV_5x5_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_OP_INV_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_OP_INV_5x5_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/32_Portfolios_ME_BEME_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/32_Portfolios_ME_BEME_OP_2x4x4_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/32_Portfolios_ME_BEME_INV_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/32_Portfolios_ME_BEME_INV_2x4x4_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/32_Portfolios_ME_OP_INV_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/32_Portfolios_ME_OP_INV_2x4x4_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_E-P_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_E-P_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_CF-P_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_CF-P_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_D-P_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_D-P_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_EP_2x3_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_EP_2x3_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_CFP_2x3_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_CFP_2x3_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_DP_2x3_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_DP_2x3_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Momentum_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/10_Portfolios_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_ST_Reversal_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_1_0_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_Prior_1_0_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/10_Portfolios_Prior_1_0_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_LT_Reversal_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/6_Portfolios_ME_Prior_60_13_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_Prior_60_13_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/10_Portfolios_Prior_60_13_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_AC_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_AC_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_BETA_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_BETA_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_NI_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_NI_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_VAR_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_VAR_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Portfolios_Formed_on_RESVAR_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/25_Portfolios_ME_RESVAR_5x5_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/5_Industry_Portfolios_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/5_Industry_Portfolios_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/10_Industry_Portfolios_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/10_Industry_Portfolios_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/12_Industry_Portfolios_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/12_Industry_Portfolios_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/17_Industry_Portfolios_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/17_Industry_Portfolios_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/30_Industry_Portfolios_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/30_Industry_Portfolios_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/38_Industry_Portfolios_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/38_Industry_Portfolios_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/49_Industry_Portfolios_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/49_Industry_Portfolios_Wout_Div_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/ME_Breakpoints_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/BE-ME_Breakpoints_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/OP_Breakpoints_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/INV_Breakpoints_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/E-P_Breakpoints_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/CF-P_Breakpoints_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/D-P_Breakpoints_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Prior_2-12_Breakpoints_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_3_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_3_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_3_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_3_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_3_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_3_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_5_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_5_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_5_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_5_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_5_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_5_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_Mom_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_Mom_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_Mom_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_Mom_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_MOM_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_Mom_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_6_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_6_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_6_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_6_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_6_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_25_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_25_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_25_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_25_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_25_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_6_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_6_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_6_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_6_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_6_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_6_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_25_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_25_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_25_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_25_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_25_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_25_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_6_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_6_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_6_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_6_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_6_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_6_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_25_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_25_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_25_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_25_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_25_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_25_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_6_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_6_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_6_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_6_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_6_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_25_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_25_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_25_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_25_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_25_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_32_Portfolios_ME_BE-ME_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_32_Portfolios_ME_BE-ME_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_32_Portfolios_ME_BE-ME_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_32_Portfolios_ME_BE-ME_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_32_Portfolios_ME_BE-ME_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_32_Portfolios_ME_INV(TA)_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Developed_ex_US_32_Portfolios_ME_INV(TA)_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Europe_32_Portfolios_ME_INV(TA)_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Asia_Pacific_ex_Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/North_America_32_Portfolios_ME_INV(TA)_OP_2x4x4_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_5_Factors_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_MOM_Factor_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_Markets_6_Portfolios_ME_BE-ME_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_Markets_6_Portfolios_ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_Markets_6_Portfolios_ME_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_Markets_6_Portfolios_ME_Prior_12_2_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_Markets_4_Portfolios_BE-ME_OP_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_Markets_4_Portfolios_OP_INV_CSV.zip'
trying URL 'http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/Emerging_Markets_4_Portfolios_BE-ME_INV_CSV.zip'
Step 3: Start processing 193 csv-files

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 load(tempf2)
 ff<-FFdownload$x_Developed_ex_US_3_Factors$monthly$Temp2
 sff<-ff["2015/2019"]
 sff<-timetk::tk_tbl(ff)
 colnames(sff)[1]<-"date"
sff
require(tidyquant)
require(timetk)
Loading required package: timetk
anyNA(sp100_daily_RI)
[1] FALSE
sp100_daily_RI_prices <- gather(sp100_daily_RI, key = symbol, value= prices, "AMAZON.COM":"CHARTER COMMS.CL.A")
anyNA(sp100_daily_RI_prices)
[1] FALSE
sp100_returns_RI_60_long <- sp100_daily_RI_prices %>% mutate(prices = as.numeric(prices)) %>% group_by(symbol) %>%
  tq_transmute(select = prices,
               mutate_fun = periodReturn, 
               period="monthly", 
               type="arithmetic",
               col_rename = "Stock.returns") %>% ungroup() %>% mutate(date = as.yearmon(date))
 anyNA(sp100_returns_RI_60_long)
[1] FALSE
 sp100_returns_RI_60_long <- sp100_returns_RI_60_long[c(2,1,3)] %>% group_by(symbol)
 
 fama_french <- sff %>%
    select(date, Mkt.RF, RF) %>% mutate(date = as.yearmon(date))
  1. Now subtract the risk-free rate from all the stocks. Then estimate each stocks beta with the market: Regress all stock excess returns on the market excess return and save all betas (optimally use mutate and map in combination with lm). Estimate the mean-return for each stock and plot the return/beta-combinations. Create the security market line and include it in the plot! What do you find?
 library(tidyquant)
 library(tidyverse)
 library(PerformanceAnalytics)


 joined_data <- left_join(sp100_returns_RI_60_long, fama_french, by= c("date"))

 joined_data <- mutate(joined_data, 
       monthly_ret_rf = Stock.returns - RF)

 require(xts)
 regr_fun <- function(data_xts) {
    lm(monthly_ret_rf ~ Mkt.RF, data = as_data_frame(data_xts)) %>%
        coef()
 }

 beta_alpha <- joined_data %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 60,
              FUN        = regr_fun,
              by.column  = FALSE,
              col_rename = c("alpha", "beta"))
Problem with `mutate()` input `nested.col`.
ℹ `as_data_frame()` is deprecated as of tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.
ℹ Input `nested.col` is `purrr::map(...)`.
ℹ The error occurred in group 1: symbol = "3M".`as_data_frame()` is deprecated as of tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.
 beta_alpha
 beta_alpha_filter <- filter(beta_alpha, !is.na(alpha))
 symbol_beta_alpha <- beta_alpha_filter %>%
    select(symbol, alpha, beta)
 symbol_beta_alpha 
 alpha <- beta_alpha %>%
    select(symbol, alpha)
 beta <- beta_alpha_filter %>%
    select(symbol, beta)
 beta

Correction:

 library(dplyr)
 means_sp100_RI_60 <- joined_data %>%
    dplyr::group_by(symbol) %>%
    dplyr::summarize(mu = mean(monthly_ret_rf, na.rm=TRUE))
`summarise()` ungrouping output (override with `.groups` argument)
 means_sp100_RI_60
 mu.hat <- mutate(beta_alpha, 
       mu_capm = beta * mean(Mkt.RF))

 mu.hat <- filter(mu.hat, !is.na(alpha))
 mu.hat <- mu.hat  %>%
    select(symbol, alpha, beta, mu_capm)

 mu.hat <- merge(mu.hat, means_sp100_RI_60)

 sml.fit <- lm(mu_capm~beta, mu.hat)

 install.packages("plotly")
Error in install.packages : Updating loaded packages
 library(plotly)

Attaching package: ‘plotly’

The following objects are masked from ‘package:plyr’:

    arrange, mutate, rename, summarise

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout
 p <- plot_ly(mu.hat, x = ~beta, y = ~mu_capm, type = 'scatter', mode = 'line', text = ~paste('symbol:', symbol)) %>%
    add_markers(x = ~beta, y = ~mu)

 p
`arrange_()` is deprecated as of dplyr 0.7.0.
Please use `arrange()` instead.
See vignette('programming') for more help
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.
  1. In a next step (following both documents), we sort the stocks according to their beta and build ten value-weighted portfolios (with more or less the same number of stocks). Repeat a) for the ten portfolios. What do you observe?
 sp100_monthly_MV <- read_excel("sp100 monthly MV.xlsx")
 head(sp100_monthly_MV, n=10)

 anyNA(sp100_monthly_MV)
[1] FALSE
 sp100_monthly_MV <- gather(sp100_monthly_MV, key = symbol, value= value, "AMAZON.COM":"CHARTER COMMS.CL.A")
 anyNA(sp100_daily_RI_prices)
[1] FALSE

mean value

 mean_sp100_MV <- sp100_monthly_MV %>% 
    group_by(symbol) %>%
    summarize(mean_value = mean(value, na.rm=TRUE))
 symbol_beta_alpha_value <- merge(mean_sp100_MV, beta)
 symbol_beta_alpha_value <- arrange(symbol_beta_alpha_value, beta)
 symbol_beta_alpha_value

create weights

 Portfolio1 <- symbol_beta_alpha_value[1:10,]
 sum_weights1 <- sum(Portfolio1$mean_value)
 weight_portfolio1 <- Portfolio1$mean_value/sum_weights1

 Portfolio2 <- symbol_beta_alpha_value[11:20,]
 sum_weights2 <- sum(Portfolio2$mean_value)
 weight_portfolio2 <- Portfolio2$mean_value/sum_weights2

 Portfolio3 <- symbol_beta_alpha_value[21:30,]
 sum_weights3 <- sum(Portfolio3$mean_value)
 weight_portfolio3 <- Portfolio3$mean_value/sum_weights3

 Portfolio4 <- symbol_beta_alpha_value[31:40,]
 sum_weights4 <- sum(Portfolio4$mean_value)
 weight_portfolio4 <- Portfolio4$mean_value/sum_weights4

 Portfolio5 <- symbol_beta_alpha_value[41:50,]
 sum_weights5 <- sum(Portfolio5$mean_value)
 weight_portfolio5 <- Portfolio5$mean_value/sum_weights5

 Portfolio6 <- symbol_beta_alpha_value[51:60,]
 sum_weights6 <- sum(Portfolio6$mean_value)
 weight_portfolio6 <- Portfolio6$mean_value/sum_weights6

 Portfolio7 <- symbol_beta_alpha_value[61:70,]
 sum_weights7 <- sum(Portfolio7$mean_value)
 weight_portfolio7 <- Portfolio7$mean_value/sum_weights7

 Portfolio8 <- symbol_beta_alpha_value[71:80,]
 sum_weights8 <- sum(Portfolio8$mean_value)
 weight_portfolio8 <- Portfolio8$mean_value/sum_weights8

 Portfolio9 <- symbol_beta_alpha_value[81:90,]
 sum_weights9 <- sum(Portfolio9$mean_value)
 weight_portfolio9 <- Portfolio9$mean_value/sum_weights9

 Portfolio10 <- symbol_beta_alpha_value[91:nrow(symbol_beta_alpha_value),]
 sum_weights10 <- sum(Portfolio10$mean_value)
 weight_portfolio10 <- Portfolio10$mean_value/sum_weights10

returns Portfolio

 sp100_returns_RI_60_wide <- sp100_returns_RI_60_long %>% spread(symbol, Stock.returns)

 portfolio1_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio1$symbol)]
 portfolio1_returns_long <- gather(portfolio1_returns_wide, key = symbol, value= returns, c(Portfolio1$symbol))
 portfolio1_returns<- portfolio1_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio1,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 1")

 portfolio2_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio2$symbol)]
 portfolio2_returns_long <- gather(portfolio2_returns_wide, key = symbol, value= returns, c(Portfolio2$symbol))
 portfolio2_returns<- portfolio2_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio2,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 2")

 portfolio3_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio3$symbol)]
 portfolio3_returns_long <- gather(portfolio3_returns_wide, key = symbol, value= returns, c(Portfolio3$symbol))
 portfolio3_returns<- portfolio3_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio3,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 3")

 portfolio4_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio4$symbol)]
 portfolio4_returns_long <- gather(portfolio4_returns_wide, key = symbol, value= returns, c(Portfolio4$symbol))
 portfolio4_returns<- portfolio4_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio4,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 4")

 portfolio5_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio5$symbol)]
 portfolio5_returns_long <- gather(portfolio5_returns_wide, key = symbol, value= returns, c(Portfolio5$symbol))
 portfolio5_returns<- portfolio5_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio5,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 5")

 portfolio6_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio6$symbol)]
 portfolio6_returns_long <- gather(portfolio6_returns_wide, key = symbol, value= returns, c(Portfolio6$symbol))
 portfolio6_returns<- portfolio6_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio6,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 6")

 portfolio7_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio7$symbol)]
 portfolio7_returns_long <- gather(portfolio7_returns_wide, key = symbol, value= returns, c(Portfolio7$symbol))
 portfolio7_returns<- portfolio7_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio7,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 7")

 portfolio8_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio8$symbol)]
 portfolio8_returns_long <- gather(portfolio8_returns_wide, key = symbol, value= returns, c(Portfolio8$symbol))
 portfolio8_returns<- portfolio8_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio8,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 8")

 portfolio9_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio9$symbol)]
 portfolio9_returns_long <- gather(portfolio9_returns_wide, key = symbol, value= returns, c(Portfolio9$symbol))
 portfolio9_returns<- portfolio9_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio9,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 9")

 portfolio10_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio10$symbol)]
 portfolio10_returns_long <- gather(portfolio10_returns_wide, key = symbol, value= returns, c(Portfolio10$symbol))
 portfolio10_returns<- portfolio10_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio10,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 10")

 allportfolio_returns <- rbind(portfolio1_returns, portfolio2_returns, portfolio3_returns, portfolio4_returns, portfolio5_returns, portfolio6_returns, portfolio7_returns, portfolio8_returns, portfolio9_returns, portfolio10_returns)

 allportfolio_returns <- allportfolio_returns %>% group_by(Portfolio)
 allportfolio_returns
 joined_data_portfolio <- left_join(allportfolio_returns, fama_french, by= c("date"))

 joined_data_portfolio <- mutate(joined_data_portfolio, 
       monthly_ret_rf = Portfolio.returns - RF)

 require(xts)
 regr_fun_portfolio <- function(data_xts) {
    lm(monthly_ret_rf ~ Mkt.RF, data = as_data_frame(data_xts)) %>%
        coef()
 }

 beta_alpha_portfolio <- joined_data_portfolio %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 60,
              FUN        = regr_fun_portfolio,
              by.column  = FALSE,
              col_rename = c("alpha", "beta"))
 beta_alpha_portfolio
 beta_alpha_portfolio_filter <- filter(beta_alpha_portfolio, !is.na(alpha))
 symbol_beta_alpha_portfolio <- beta_alpha_portfolio_filter %>%
    select(Portfolio, alpha, beta)
 symbol_beta_alpha_portfolio
 alpha_portfolio <- beta_alpha_portfolio %>%
    select(Portfolio, alpha)
 beta_portfolio <- beta_alpha_portfolio %>%
    select(Portfolio, beta)

Correction:

 means_Portfolio <- joined_data_portfolio %>%
    dplyr::group_by(Portfolio) %>%
    dplyr::summarize(mu = mean(monthly_ret_rf, na.rm=TRUE))
`summarise()` ungrouping output (override with `.groups` argument)
 means_Portfolio
 return_beta_portfolio <- merge(means_Portfolio, beta_portfolio)

 mu.hat_portfolio <- mutate(beta_alpha_portfolio, 
       mu_capm_portfolio = beta * mean(Mkt.RF))

 mu.hat_portfolio <- filter(mu.hat_portfolio, !is.na(alpha))
 mu.hat_portfolio <- mu.hat_portfolio  %>%
    select(Portfolio, alpha, beta, mu_capm_portfolio)

 mu.hat_portfolio <- merge(mu.hat_portfolio, means_Portfolio)

 sml.fit_portfolio <- lm(mu_capm_portfolio~beta, mu.hat_portfolio)

 library(plotly)

 p_portfolio <- plot_ly(mu.hat_portfolio, x = ~beta, y = ~mu_capm_portfolio, type = 'scatter', mode = 'line', text = ~paste('Portfolio:', Portfolio)) %>%
    add_markers(x = ~beta, y = ~mu)

 p_portfolio
  1. In the third step you follow page 6-8 of the second document and estimate the second-pass regression with the market and then market & idiosyncratic risk. What do you observe? Present all your results in a similar fashion as in the document.
 require(xts)
 regr_fun_residuals <- function(data_xts) {
    data_xts <- lm(monthly_ret_rf ~ Mkt.RF, data = as_data_frame(data_xts))
 R <- summary(data_xts)$sigma^2
 return(R)
 }

 residuals <- joined_data %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 60,
              FUN        = regr_fun_residuals,
              by.column  = FALSE,
              col_rename = c("Residuals"))

 residuals_only <- filter(residuals, !is.na(Residuals))
 symbol_residuals <- residuals_only %>%
    select(symbol, Residuals)

 mean_MKt.RF <- joined_data %>%
    group_by(symbol) %>%
    summarize(mu_MKt_RF = mean(Mkt.RF, na.rm=TRUE))

 first <- left_join(symbol_beta_alpha, symbol_residuals, by = "symbol" )

 second <- merge(mean_MKt.RF, means_sp100_RI_60)

 all_inputs <- merge(first, second)

 second_pass_regression <- lm(mu~ beta + Residuals, all_inputs)

 summary(second_pass_regression)

Call:
lm(formula = mu ~ beta + Residuals, data = all_inputs)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0259042 -0.0053882 -0.0002212  0.0053325  0.0307786 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.065691   0.003284 -20.004   <2e-16 ***
beta         0.200369   0.266416   0.752    0.454    
Residuals    0.173445   0.388579   0.446    0.656    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.008921 on 94 degrees of freedom
Multiple R-squared:  0.01422,   Adjusted R-squared:  -0.006757 
F-statistic: 0.6778 on 2 and 94 DF,  p-value: 0.5102
 p
 require(xts)
 regr_fun_residuals <- function(data_xts) {
    data_xts <- lm(monthly_ret_rf ~ Mkt.RF, data = as_data_frame(data_xts))
 R <- summary(data_xts)$sigma^2
 return(R)
 }

 residuals_portfolio <- joined_data_portfolio %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 60,
              FUN        = regr_fun_residuals,
              by.column  = FALSE,
              col_rename = c("Residuals"))

 residuals_only_portfolio <- filter(residuals_portfolio, !is.na(Residuals))
 symbol_residuals_portfolio <- residuals_only_portfolio %>%
    select(Portfolio, Residuals)

 mean_MKt.RF_portfolio <- joined_data_portfolio %>%
    group_by(Portfolio) %>%
    summarize(mu_MKt_RF = mean(Mkt.RF, na.rm=TRUE))

 first_portfolio <- left_join(symbol_beta_alpha_portfolio, symbol_residuals_portfolio, by = "Portfolio" )

 second_portfolio <- merge(mean_MKt.RF_portfolio, means_Portfolio)

 all_inputs_portfolio <- merge(first_portfolio, second_portfolio)

 second_pass_regression_portfolio <- lm(mu~ beta + Residuals, all_inputs_portfolio)

 summary(second_pass_regression_portfolio)

Call:
lm(formula = mu ~ beta + Residuals, data = all_inputs_portfolio)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0045091 -0.0012808  0.0001732  0.0014667  0.0056631 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.078676   0.006378 -12.335 5.28e-06 ***
beta         0.133825   0.354192   0.378   0.7168    
Residuals    2.506199   1.234101   2.031   0.0818 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.003436 on 7 degrees of freedom
Multiple R-squared:  0.5783,    Adjusted R-squared:  0.4579 
F-statistic:   4.8 on 2 and 7 DF,  p-value: 0.04869
 p_portfolio

Exercise 2: Performance Evaluation I

Read Chapter 24 of our book. In this exercise use a Minimum Variance and a Tangecy (maxium Sharpe Ratio) portfolio calculate from your stocks, as well as the S&P500 as a benchmark (Period 2000-01-01 - 2020-01-11). For all three Investment Opportunities imagine you invest 100USD per month into the portfolio. What is the overall return this investment provides you? How much should you have investd at the bginning (one-time investment) to get the eact same overall wealth at the end of 2020? Can you plot both wealth developments over time?

First of all, I am going to download all the crucial data, which we need in order to create the three portfolios. My personal stock choice are Apple, Nividia, Microsoft, American Express, Walmart, Bank of America, Morgan Stanley, Disney, Exxon Mobile. As the benchmark we take the S&P500, regarding to the underling task.

install.packages("FinCal",dependencies=TRUE)
library(timetk, PortfolioAnalytics)
SP500 <- tq_index("SP500")
stock_names <- c("AAPL", "NVDA", "MSFT", "AXP", "WMT", "BAC", "GS","MS", "DIS", "XOM")
stock_returns <-tq_get(x = stock_names,get  = "stock.prices", from = "2000-01-01", to   = "2020-11-18") %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly")
unique(stock_returns$symbol)

stock_returns_xts <- stock_returns %>%
                      subset( select = c(symbol,date, monthly.returns)) %>% 
                      pivot_wider(names_from = symbol, 
                                  values_from = monthly.returns) %>% 
                      tk_xts(date_var = date, silent = TRUE)
stock_returns_xts
port <- portfolio.spec(assets=stock_names)%>%
  add.constraint(type="full_investment") %>% #In our approach we use full_investment as our task it is
  add.constraint(type="long_only") %>% #We do not take any short-selling
  add.objective(type="return", name="mean")
port

Now I defined the investment constrains for our investments, which is based on our task a full investment constrained, due to the fact that we would like to be fully invested the whole period. The long only constrain comes from the idea that we are just caring about the upper part of the efficient frontier, in order to construct the tangency portfolio, which is the most efficient way to set our 10 stocks together concerning the faced risked. So, in order words the tangency portfolio maximizes the Sharpe Ratio. The second portfolio we have to construct is the Minimum-Variance Portfolio. The “third” portfolio would be an absolute passive strategy we would just invest in the SP500.

opt_port <- optimize.portfolio(R=stock_returns_xts, portfolio=port,
                                 optimize_method="ROI", trace=TRUE)
opt_port

So, as we see we would make the most money if we would just everything in Nividia. However, we do not look for the maximum return, we look for the maximum return in relation to the taken risk.

plot(opt_port, chart.assets=TRUE, main="Maximum Return", 
      xlim=c(0,0.3), ylim=c(0,0.5))
chart.RiskReward(opt_port,return.col="mean", risk.col="sd",
                 chart.assets=TRUE, 
                 xlim=c(0, 0.25),
                 main="Maximum Return")
frontier <- create.EfficientFrontier(R=stock_returns_xts, 
                                       portfolio=port, 
                                       type="mean-StdDev")
chart.EfficientFrontier(frontier, match.col="StdDev", type="l",rf = 0, tangent.line = TRUE, chart.assets = TRUE,)

The efficient frontier is the set of optimal portfolios that offer the highest expected return for a defined level of risk or the lowest risk for a given level of expected return. Portfolios that lie below the efficient frontier are sub-optimal because they do not provide enough return for the level of risk.

port_tan <- portfolio.spec(assets=stock_names) %>%
  add.constraint(type="full_investment") %>%
  add.constraint(type = "long_only") %>%
  add.objective(type="return", name="mean")
port_tan
init.portf <- portfolio.spec(assets=stock_names)
init.portf <- add.constraint(portfolio=init.portf, type="full_investment")
init.portf <- add.constraint(portfolio=init.portf, type="long_only")
init.portf <- add.objective(portfolio=init.portf, type="return", name="mean")
init.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")
init.portf

Maximizing the Sharpe Ratio can be formulated as a quadratic programming problem and solved very quickly using optimize_method=“ROI”. Although “StdDev” was specified as an objective, the quadratic programming problem uses the variance-covariance matrix in the objective function.

The default actin if “mean” and “StdDev” are specified as objectives with optimize_method=“ROI” is to maximize quadratic utility. If we want to maximize Sharpe Ratio, we need to pass in maxSR=TRUE to optimize the portfolio, which should lead us to the tangency portfolio.

maxSR.lo.ROI <- optimize.portfolio(R=stock_returns_xts, portfolio=init.portf, optimize_methode="ROI", maxSR=TRUE, trace=TRUE, search_size= 1000000)
maxSR.lo.ROI

It needed 17 Iterations to come as close as possible to the tangeny portfolio. We would put a big stake into Walmart, Exxon-Mobil, Disney. Although the maximum Sharpe Ratio objective can be solved quickly and accurately with optimize_method=“ROI”, it is also possible or DEoptim. These solvers have the added flexibility of using different methods to calculate the Sharpe Ratio (e.g. we could specify annualized measures of risk and return).

maxSR.lo.RP <- optimize.portfolio(R=stock_returns_xts, portfolio=init.portf, optimize_method="random", search_size=1000000, trace=TRUE)
maxSR.lo.RP
chart.RiskReward(maxSR.lo.RP, risk.col="StdDev", return.col="mean")
maxSR.lo.DE <- optimize.portfolio(R=stock_returns_xts, portfolio=init.portf, optimize_method="DEoptim", search_size=1000000, trace=TRUE)
maxSR.lo.DE
chart.RiskReward(maxSR.lo.DE, risk.col="StdDev", return.col="mean")

The Minmimum Variance Portfolio minimizing the variance of the portfolio

port_minvar <- portfolio.spec(assets=stock_names) %>%
  add.constraint(type="full_investment") %>%
  add.constraint(type = "long_only") %>%
  add.objective(type="risk", name="var")
port_minvar
opt_minvar <- optimize.portfolio(R = stock_returns_xts,portfolio = port_minvar,
                              optimize_method = "ROI", trace = TRUE, search_size= 1000000)
opt_minvar

So, as we can see we see that we would invest the majority of our capital in Walmart and Exxon Mobile, due to the low standard deviation and quiet interesting we would not invest in Nividia.

chart.RiskReward(opt_minvar,return.col="mean", risk.col="sd",
                 chart.assets=TRUE, 
                 xlim=c(0.01, 0.25),
                 main="Minimum Variance")
wts_tan <- c(0.104, 0.002, 0.120, 0.002, 0.444, 0.006, 0.026, 0.000, 0.094, 0.202)
wts_minvar <- c(0.0241, 0.0000, 0.0615, 0.0000, 0.4551, 0.0000, 0.0000, 0.0000, 0.1094, 0.3499)# Weights from the Minimum Variance Portfolio

Portfolio Returns

stock_returns

tan_port <- stock_returns %>%
    tq_portfolio(assets_col  = symbol, 
                 returns_col = monthly.returns, 
                 weights     = wts_tan, 
                 col_rename  = "Return")
tan_port
minvar_port <- stock_returns %>%
    tq_portfolio(assets_col  = symbol, 
                 returns_col = monthly.returns, 
                 weights     = wts_minvar, 
                 col_rename  = "Return")
minvar_port

For this task we take the SP500 as the benchmark.

bench_returns <- "^GSPC" %>%
    tq_get(get  = "stock.prices",
           from = "2000-01-01",
           to   = "2020-11-17") %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Benchmark")
bench_returns
ov_tan_port <- left_join(tan_port,bench_returns,by = "date")
ov_minvar_port <- left_join(minvar_port,bench_returns,by = "date")

ov_tan_port
ov_minvar_port
ov_tan_port %>%
    tq_performance(Ra = Return, Rb = Benchmark, 
    performance_fun = table.CAPM)
ov_minvar_port %>%
    tq_performance(Ra = Return, Rb = Benchmark, 
    performance_fun = table.CAPM)
sharp_tan <- (0.1112-0)/sd(ov_tan_port$Return)
sharp_minvar <- (0.0605-0)/sd(ov_minvar_port$Return)
sharp_bench <- 1 #Checked it on the internet on an average base it is close to 1
ov_sharp <- cbind(sharp_tan, sharp_minvar, sharp_bench)
ov_sharp
tan_r <- geometric.mean(ov_tan_port$Return)
minvar_r <- geometric.mean(ov_minvar_port$Return)
bench_r <- geometric.mean(ov_minvar_port$Benchmark)
FV_tan <- FV(rate = tan_r, nper = 1:239,pv = 100,pmt = -100,type = 1)
FV_minvar <- FV(rate = minvar_r, nper = 1:239, pv = 100, pmt = -100, type=1)
FV_bench <- FV(rate=bench_r, nper = 1:239, pv = 100, pmt = -100, type = 1)
id <- c(1:239)
as.data.frame(FV_tan)
as.data.frame(id)
as.data.frame(FV_minvar)
as.data.frame(FV_bench)
ov_list <- cbind(id, FV_tan,FV_minvar,FV_bench)
ov_list <- as.data.frame(ov_list)
invest <- ov_list[239,]
invest
ggplot(ov_list, aes(x=id)) + 
  geom_line(aes(y = FV_tan), color = "darkred") + 
  geom_line(aes(y = FV_minvar), color="steelblue", linetype="twodash") +
  geom_line(aes(y=FV_bench), color="pink") +
  ggtitle("Wealth Development")

The tangency portfolio performed very good related to the others.

PV_tan <- PV(rate = tan_r,nper = 1:239,fv =-invest$FV_tan ,pmt = 0,type = 1)
PV_minvar <- PV(rate =minvar_r, nper=1:239,fv=-invest$FV_minvar,pmt=0,type=1)
PV_bench <- PV(rate=bench_r, nper=1:239, fv=-invest$FV_bench,pmt=0, type=1)
id <- c(1:239)
as.data.frame(PV_tan)
as.data.frame(id)
as.data.frame(PV_minvar)
as.data.frame(PV_bench)
ov_list <- cbind(id, PV_tan,PV_minvar,PV_bench)
ov_list <- as.data.frame(ov_list)
invest <- ov_list[239,]
invest

As we see if we would like to make an one time investment, but also the exact same amount of money as if we would invest 100 dollar per month. For the Tangency Portfolio we would need to invest 3109.721$ for the Minimium Variance Portfolio we would need to invest 4695.70$ and for the passive strategy S&P500 we would need 5082.05. So, as we can see that the intrest of intrest effect takes here part and dramatically increases the future value of our investment.

ggplot(ov_list, aes(x=id)) + 
  geom_line(aes(y = PV_tan), color = "darkred") + 
  geom_line(aes(y = PV_minvar), color="steelblue", linetype="twodash") +
  geom_line(aes(y=PV_bench), color="pink") +
  ggtitle("One time investment")

Exercise 3: Performance Evaluation II

For the same two portfolios and the appropriate benchmark calculate overall performance measures (Sharpe ratio, M2 [assume a risk-fre rate of 0], Treynor Ratio, Jensen’s Alpha and Information ratio). Interpret. Additional do the two market timing regressions (ch 24.4) and see whether your portfolios can “time” the market.

Exercise 4: Active Portfolio Management

Work through trough the demo demo(relative_ranking). Use what you lear here, form an appropriate opinion on the ranking of your assets and optimize a Minimum Variance and Maximum Sharpe ratio Portfolio. Which one performs better?

demo(relative_ranking)

data(edhec)

library(PortfolioAnalytics)
data(edhec)
R <- edhec[,1:4]
funds <- colnames(R)

#’ Construct initial portfolio with basic constraints.

init.portf <- portfolio.spec(assets=funds)
init.portf <- add.constraint(portfolio=init.portf, type="weight_sum", 
                              min_sum=0.99, max_sum=1.01)
init.portf <- add.constraint(portfolio=init.portf, type="box",min=0.05, max=0.5)
init.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")

init.portf <- add.objective(portfolio=init.portf, type="return", name="mean")

init.portf

#’ Here we express views on the relative rank of the asset returns.

asset.rank <- c(2, 3, 1, 4)

#’ Use Meucci Fully Flexible Views framework to express views on the relative #’ order of asset returns. #’ Define prior probabilities.

p <- rep(1 / nrow(R), nrow(R))

#’ Express view on the relative ordering of asset returns

m.moments <- meucci.ranking(R, p, asset.rank)

#’ Express views using the method described in Almgren and Chriss, > #’ “Portfolios from Sorts”.

 ac.moments <- list()

ac.moments
ac.moments$mu <- ac.ranking(R, asset.rank)

Sample estimate for second moment

ac.moments$sigma <- cov(R)

#’ Generate random portfolios for use in the optimization.

rp <- random_portfolios(init.portf, 5000)

rp

#’ Run the optimization using first and second moments estimated from #’ Meucci’s Fully Flexible Views framework using the moments we calculated

opt.meucci <- optimize.portfolio(R, 
                                 init.portf,
                                 optimize_method="random", 
                                 rp=rp, 
                                 trace=TRUE,
                                 momentargs=m.moments)

opt.meucci

#’ Run the optimization using first moment estimated based on Almgren and Chriss, #’ “Portfolios from Sorts”. The second moment uses the sample estimate.

opt.ac <- optimize.portfolio(R,
                             init.portf,
                             optimize_method="random",
                             rp=rp,
                             trace=TRUE,
                             momentargs=ac.moments)

opt.ac

#’ For comparison, run the optimization using sample estimates for first and #’ second moments.

opt.sample <- optimize.portfolio(R, 
                                 init.portf, 
                                 optimize_method="random", 
                                 rp=rp,
                                 trace=TRUE)
opt.sample

#’ Here we plot the optimal weights of each optimization.

chart.Weights(combine.optimizations(list(meucci=opt.meucci, 
                                           ac=opt.ac,
                                           sample=opt.sample)), 
                
              
ylim=c(0,1), plot.type="barplot")

#’ Here we define a custom moment function to estimate moments based on #’ relative ranking views. #’ Asset are ranked according to a momentum or reversal view based on the #’ previous n periods.

moment.ranking <- function(R, n=1, momentum=TRUE, method=c("meucci", "ac")){
  
# Moment function to estimate moments based on relative ranking of 
# expected returns.
  
method <- match.arg(method)
   
   # Use the most recent n periods of returns
   tmpR <- apply(tail(R, n), 2, function(x) prod(1 + x) - 1)
   
   if(momentum){
     # Assume that the assets with the highest return will continue to outperform
     asset.rank <- order(tmpR)
   } else {
# Assume that the assets with the highest return will reverse
asset.rank <- rev(order(tmpR))
}
switch(method,
meucci = {
            # Meucci Fully Flexible Views framework
            # Prior probabilities
           p <- rep(1 / nrow(R), nrow(R))
            
            # Relative ordering view
           moments <- meucci.ranking(R, p, asset.rank)
          },
          ac = {
            # Almgren and Chriss Portfolios from Sorts
            moments <- list()
            moments$mu <- ac.ranking(R, asset.rank)
            # Sample estimate for second moment
            moments$sigma <- cov(R)
         }
   )
   return(moments)
 }

#’ Here we run out of sample backtests to test the out of sample performance using the different frameworks to express our views on relative asset return ranking.

opt.bt.meucci <- optimize.portfolio.rebalancing(R, init.portf, 
                                               optimize_method="random",  
                                               rebalance_on="quarters", 
                                               training_period=100,
                                               rp=rp,
                                               momentFUN="moment.ranking",
                                               n=2,
                                               momentum=TRUE,
                                               method="meucci")
opt.bt.ac <- optimize.portfolio.rebalancing(R, init.portf, 
optimize_method="random", 
rebalance_on="quarters", 
rp=rp,
momentFUN="moment.ranking",
n=2,
momentum=TRUE,
method="ac")

opt.bt.sample <- optimize.portfolio.rebalancing(R, init.portf, 
                                                 optimize_method="random", 
                                                 rebalance_on="quarters", 
                                                 training_period=100,
                                                 rp=rp)

#’ Compute returns and chart performance summary.

ret.meucci <- Return.portfolio(R, extractWeights(opt.bt.meucci))
ret.ac <- Return.portfolio(R, extractWeights(opt.bt.ac))
ret.sample <- Return.portfolio(R, extractWeights(opt.bt.sample))
ret <- cbind(ret.meucci, ret.ac, ret.sample)
colnames(ret) <- c("meucci.rank", "ac.rank", "sample")
charts.PerformanceSummary(ret, main="Ranking Views Performance")
---
title: "Portfoliomanagement and Financial Analysis - Assignment 7"
subtitle: "Submit until Monday 2020-11-23, 13:00"
author: "Markovic, Mitschel"
output: html_notebook
---
  
```{r setup}
#remotes::install_github("braverock/FactorAnalytics",  build_vignettes = TRUE, force = TRUE)
pacman::p_load(tidyverse,tidyquant,FFdownload,FactorAnalytics,PerformanceAnalytics,PortfolioAnalytics,nloptr)
```

**Please** remember to put your assignment solutions in `rmd` format using **many** chunks and putting readable text in between, similar to my examples given in Research Methods and Assignment 1!

For all exercises: Please use the Assignment-Forum to post your questions, I will try my best to help you along!

## Exercise 1: Analysing the CAPM

In this exercise we want to estimate the CAPM. Please read carefully through the two documents provided (right hand side: files). Then we start to collect the necessary data:
  
a) From Datastream get the last 10 years of data from the 100 stocks of the S&P100 using the list `LS&P100I` (S&P 100): total return index (RI) and market cap (MV)


```{r}
library(readxl)
sp100_daily_RI <- read_excel("sp100 daily RI_2.xlsx")
head(sp100_daily_RI, n=10)
```

```{r}
library(readxl)
sp100_monthly_MV <- read_excel("sp100 monthly MV.xlsx")
head(sp100_monthly_MV, n=10)
```


b) Further import the Fama-French-Factors from Kenneth Frenchs homepage (monthly, e.g. using `FFdownload`). From both datasets we select data for the last (available) 60 months, calculate returns (simple percentage) for the US-Stocks and eliminate those stocks that have NAs for this period.

```{r}
 tempf <- tempfile(fileext = ".RData"); tempd <- tempdir(); temptxt <- tempfile(fileext = ".txt")
 inputlist <- c("F-F_Research_Data_Factors","F-F_Market_Beta")
 FFdownload(exclude_daily=TRUE,tempdir=tempd,download=TRUE,download_only=FALSE,inputlist=inputlist)
 tempf2 <- tempfile(fileext = ".RData"); tempd2 <- tempdir() 
 FFdownload(output_file = tempf2,tempdir = tempd2,exclude_daily = TRUE, download = TRUE, download_only=FALSE, listsave=temptxt)
 load(tempf2)
 ff<-FFdownload$x_Developed_ex_US_3_Factors$monthly$Temp2
 sff<-ff["2015/2019"]
 sff<-timetk::tk_tbl(ff)
 colnames(sff)[1]<-"date"
```


```{r}
sff
```

```{r}
require(tidyquant)
require(timetk)

anyNA(sp100_daily_RI)
sp100_daily_RI_prices <- gather(sp100_daily_RI, key = symbol, value= prices, "AMAZON.COM":"CHARTER COMMS.CL.A")
anyNA(sp100_daily_RI_prices)

```

```{r}
sp100_returns_RI_60_long <- sp100_daily_RI_prices %>% mutate(prices = as.numeric(prices)) %>% group_by(symbol) %>%
  tq_transmute(select = prices,
               mutate_fun = periodReturn, 
               period="monthly", 
               type="arithmetic",
               col_rename = "Stock.returns") %>% ungroup() %>% mutate(date = as.yearmon(date))
 anyNA(sp100_returns_RI_60_long)

 sp100_returns_RI_60_long <- sp100_returns_RI_60_long[c(2,1,3)] %>% group_by(symbol)
 
 fama_french <- sff %>%
    select(date, Mkt.RF, RF) %>% mutate(date = as.yearmon(date))
```


c) Now subtract the risk-free rate from all the stocks. Then estimate each stocks beta with the market: Regress all stock excess returns on the market excess return and save all betas (optimally use `mutate` and `map` in combination with `lm`). Estimate the mean-return for each stock and plot the return/beta-combinations. Create the security market line and include it in the plot! What do you find?

```{r}
 library(tidyquant)
 library(tidyverse)
 library(PerformanceAnalytics)


 joined_data <- left_join(sp100_returns_RI_60_long, fama_french, by= c("date"))

 joined_data <- mutate(joined_data, 
       monthly_ret_rf = Stock.returns - RF)

 require(xts)
 regr_fun <- function(data_xts) {
    lm(monthly_ret_rf ~ Mkt.RF, data = as_data_frame(data_xts)) %>%
        coef()
 }

 beta_alpha <- joined_data %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 60,
              FUN        = regr_fun,
              by.column  = FALSE,
              col_rename = c("alpha", "beta"))

 beta_alpha
```

```{r}
 beta_alpha_filter <- filter(beta_alpha, !is.na(alpha))
 symbol_beta_alpha <- beta_alpha_filter %>%
    select(symbol, alpha, beta)
 symbol_beta_alpha 
```

```{r}
 alpha <- beta_alpha %>%
    select(symbol, alpha)
```

```{r}
 beta <- beta_alpha_filter %>%
    select(symbol, beta)
 beta
```
Correction:

```{r}
 library(dplyr)
 means_sp100_RI_60 <- joined_data %>%
    dplyr::group_by(symbol) %>%
    dplyr::summarize(mu = mean(monthly_ret_rf, na.rm=TRUE))
 means_sp100_RI_60
```


```{r}
 mu.hat <- mutate(beta_alpha, 
       mu_capm = beta * mean(Mkt.RF))

 mu.hat <- filter(mu.hat, !is.na(alpha))
 mu.hat <- mu.hat  %>%
    select(symbol, alpha, beta, mu_capm)

 mu.hat <- merge(mu.hat, means_sp100_RI_60)

 sml.fit <- lm(mu_capm~beta, mu.hat)

 install.packages("plotly")
 library(plotly)

 p <- plot_ly(mu.hat, x = ~beta, y = ~mu_capm, type = 'scatter', mode = 'line', text = ~paste('symbol:', symbol)) %>%
    add_markers(x = ~beta, y = ~mu)

 p
```

d) In a next step (following both documents), we sort the stocks according to their beta and build ten value-weighted portfolios (with more or less the same number of stocks). Repeat a) for the ten portfolios. What do you observe?

```{r}
 sp100_monthly_MV <- read_excel("sp100 monthly MV.xlsx")
 head(sp100_monthly_MV, n=10)

 anyNA(sp100_monthly_MV)
 sp100_monthly_MV <- gather(sp100_monthly_MV, key = symbol, value= value, "AMAZON.COM":"CHARTER COMMS.CL.A")
 anyNA(sp100_daily_RI_prices)
```

 mean value
```{r}
 mean_sp100_MV <- sp100_monthly_MV %>% 
    group_by(symbol) %>%
    summarize(mean_value = mean(value, na.rm=TRUE))
```

```{r}
 symbol_beta_alpha_value <- merge(mean_sp100_MV, beta)
```

```{r}
 symbol_beta_alpha_value <- arrange(symbol_beta_alpha_value, beta)
 symbol_beta_alpha_value
```
create weights
```{r}
 Portfolio1 <- symbol_beta_alpha_value[1:10,]
 sum_weights1 <- sum(Portfolio1$mean_value)
 weight_portfolio1 <- Portfolio1$mean_value/sum_weights1

 Portfolio2 <- symbol_beta_alpha_value[11:20,]
 sum_weights2 <- sum(Portfolio2$mean_value)
 weight_portfolio2 <- Portfolio2$mean_value/sum_weights2

 Portfolio3 <- symbol_beta_alpha_value[21:30,]
 sum_weights3 <- sum(Portfolio3$mean_value)
 weight_portfolio3 <- Portfolio3$mean_value/sum_weights3

 Portfolio4 <- symbol_beta_alpha_value[31:40,]
 sum_weights4 <- sum(Portfolio4$mean_value)
 weight_portfolio4 <- Portfolio4$mean_value/sum_weights4

 Portfolio5 <- symbol_beta_alpha_value[41:50,]
 sum_weights5 <- sum(Portfolio5$mean_value)
 weight_portfolio5 <- Portfolio5$mean_value/sum_weights5

 Portfolio6 <- symbol_beta_alpha_value[51:60,]
 sum_weights6 <- sum(Portfolio6$mean_value)
 weight_portfolio6 <- Portfolio6$mean_value/sum_weights6

 Portfolio7 <- symbol_beta_alpha_value[61:70,]
 sum_weights7 <- sum(Portfolio7$mean_value)
 weight_portfolio7 <- Portfolio7$mean_value/sum_weights7

 Portfolio8 <- symbol_beta_alpha_value[71:80,]
 sum_weights8 <- sum(Portfolio8$mean_value)
 weight_portfolio8 <- Portfolio8$mean_value/sum_weights8

 Portfolio9 <- symbol_beta_alpha_value[81:90,]
 sum_weights9 <- sum(Portfolio9$mean_value)
 weight_portfolio9 <- Portfolio9$mean_value/sum_weights9

 Portfolio10 <- symbol_beta_alpha_value[91:nrow(symbol_beta_alpha_value),]
 sum_weights10 <- sum(Portfolio10$mean_value)
 weight_portfolio10 <- Portfolio10$mean_value/sum_weights10
```

returns Portfolio
```{r}
 sp100_returns_RI_60_wide <- sp100_returns_RI_60_long %>% spread(symbol, Stock.returns)

 portfolio1_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio1$symbol)]
 portfolio1_returns_long <- gather(portfolio1_returns_wide, key = symbol, value= returns, c(Portfolio1$symbol))
 portfolio1_returns<- portfolio1_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio1,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 1")

 portfolio2_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio2$symbol)]
 portfolio2_returns_long <- gather(portfolio2_returns_wide, key = symbol, value= returns, c(Portfolio2$symbol))
 portfolio2_returns<- portfolio2_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio2,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 2")

 portfolio3_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio3$symbol)]
 portfolio3_returns_long <- gather(portfolio3_returns_wide, key = symbol, value= returns, c(Portfolio3$symbol))
 portfolio3_returns<- portfolio3_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio3,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 3")

 portfolio4_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio4$symbol)]
 portfolio4_returns_long <- gather(portfolio4_returns_wide, key = symbol, value= returns, c(Portfolio4$symbol))
 portfolio4_returns<- portfolio4_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio4,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 4")

 portfolio5_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio5$symbol)]
 portfolio5_returns_long <- gather(portfolio5_returns_wide, key = symbol, value= returns, c(Portfolio5$symbol))
 portfolio5_returns<- portfolio5_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio5,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 5")

 portfolio6_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio6$symbol)]
 portfolio6_returns_long <- gather(portfolio6_returns_wide, key = symbol, value= returns, c(Portfolio6$symbol))
 portfolio6_returns<- portfolio6_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio6,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 6")

 portfolio7_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio7$symbol)]
 portfolio7_returns_long <- gather(portfolio7_returns_wide, key = symbol, value= returns, c(Portfolio7$symbol))
 portfolio7_returns<- portfolio7_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio7,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 7")

 portfolio8_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio8$symbol)]
 portfolio8_returns_long <- gather(portfolio8_returns_wide, key = symbol, value= returns, c(Portfolio8$symbol))
 portfolio8_returns<- portfolio8_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio8,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 8")

 portfolio9_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio9$symbol)]
 portfolio9_returns_long <- gather(portfolio9_returns_wide, key = symbol, value= returns, c(Portfolio9$symbol))
 portfolio9_returns<- portfolio9_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio9,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 9")

 portfolio10_returns_wide <- sp100_returns_RI_60_wide[c("date", Portfolio10$symbol)]
 portfolio10_returns_long <- gather(portfolio10_returns_wide, key = symbol, value= returns, c(Portfolio10$symbol))
 portfolio10_returns<- portfolio10_returns_long %>%
  tq_portfolio(assets_col = symbol,
               returns_col = returns,
               weights = weight_portfolio10,
               col_rename = "Portfolio.returns") %>% mutate(Portfolio = "Portfolio 10")

 allportfolio_returns <- rbind(portfolio1_returns, portfolio2_returns, portfolio3_returns, portfolio4_returns, portfolio5_returns, portfolio6_returns, portfolio7_returns, portfolio8_returns, portfolio9_returns, portfolio10_returns)

 allportfolio_returns <- allportfolio_returns %>% group_by(Portfolio)
 allportfolio_returns
```

```{r}
 joined_data_portfolio <- left_join(allportfolio_returns, fama_french, by= c("date"))

 joined_data_portfolio <- mutate(joined_data_portfolio, 
       monthly_ret_rf = Portfolio.returns - RF)

 require(xts)
 regr_fun_portfolio <- function(data_xts) {
    lm(monthly_ret_rf ~ Mkt.RF, data = as_data_frame(data_xts)) %>%
        coef()
 }

 beta_alpha_portfolio <- joined_data_portfolio %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 60,
              FUN        = regr_fun_portfolio,
              by.column  = FALSE,
              col_rename = c("alpha", "beta"))
 beta_alpha_portfolio
```


```{r}
 beta_alpha_portfolio_filter <- filter(beta_alpha_portfolio, !is.na(alpha))
 symbol_beta_alpha_portfolio <- beta_alpha_portfolio_filter %>%
    select(Portfolio, alpha, beta)
 symbol_beta_alpha_portfolio
```

```{r}
 alpha_portfolio <- beta_alpha_portfolio %>%
    select(Portfolio, alpha)
```


```{r}
 beta_portfolio <- beta_alpha_portfolio %>%
    select(Portfolio, beta)
```

Correction:

```{r}
 means_Portfolio <- joined_data_portfolio %>%
    dplyr::group_by(Portfolio) %>%
    dplyr::summarize(mu = mean(monthly_ret_rf, na.rm=TRUE))
 means_Portfolio
```

```{r}
 return_beta_portfolio <- merge(means_Portfolio, beta_portfolio)

 mu.hat_portfolio <- mutate(beta_alpha_portfolio, 
       mu_capm_portfolio = beta * mean(Mkt.RF))

 mu.hat_portfolio <- filter(mu.hat_portfolio, !is.na(alpha))
 mu.hat_portfolio <- mu.hat_portfolio  %>%
    select(Portfolio, alpha, beta, mu_capm_portfolio)

 mu.hat_portfolio <- merge(mu.hat_portfolio, means_Portfolio)

 sml.fit_portfolio <- lm(mu_capm_portfolio~beta, mu.hat_portfolio)

 library(plotly)

 p_portfolio <- plot_ly(mu.hat_portfolio, x = ~beta, y = ~mu_capm_portfolio, type = 'scatter', mode = 'line', text = ~paste('Portfolio:', Portfolio)) %>%
    add_markers(x = ~beta, y = ~mu)

 p_portfolio
```

 e) In the third step you follow page 6-8 of the second document and estimate the second-pass regression with the market and then market & idiosyncratic risk. What do you observe? Present all your results in a similar fashion as in the document.

```{r}
 require(xts)
 regr_fun_residuals <- function(data_xts) {
    data_xts <- lm(monthly_ret_rf ~ Mkt.RF, data = as_data_frame(data_xts))
 R <- summary(data_xts)$sigma^2
 return(R)
 }

 residuals <- joined_data %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 60,
              FUN        = regr_fun_residuals,
              by.column  = FALSE,
              col_rename = c("Residuals"))

 residuals_only <- filter(residuals, !is.na(Residuals))
 symbol_residuals <- residuals_only %>%
    select(symbol, Residuals)

 mean_MKt.RF <- joined_data %>%
    group_by(symbol) %>%
    summarize(mu_MKt_RF = mean(Mkt.RF, na.rm=TRUE))

 first <- left_join(symbol_beta_alpha, symbol_residuals, by = "symbol" )

 second <- merge(mean_MKt.RF, means_sp100_RI_60)

 all_inputs <- merge(first, second)

 second_pass_regression <- lm(mu~ beta + Residuals, all_inputs)

 summary(second_pass_regression)
```

```{r}
 p
```


```{r}
 require(xts)
 regr_fun_residuals <- function(data_xts) {
    data_xts <- lm(monthly_ret_rf ~ Mkt.RF, data = as_data_frame(data_xts))
 R <- summary(data_xts)$sigma^2
 return(R)
 }

 residuals_portfolio <- joined_data_portfolio %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 60,
              FUN        = regr_fun_residuals,
              by.column  = FALSE,
              col_rename = c("Residuals"))

 residuals_only_portfolio <- filter(residuals_portfolio, !is.na(Residuals))
 symbol_residuals_portfolio <- residuals_only_portfolio %>%
    select(Portfolio, Residuals)

 mean_MKt.RF_portfolio <- joined_data_portfolio %>%
    group_by(Portfolio) %>%
    summarize(mu_MKt_RF = mean(Mkt.RF, na.rm=TRUE))

 first_portfolio <- left_join(symbol_beta_alpha_portfolio, symbol_residuals_portfolio, by = "Portfolio" )

 second_portfolio <- merge(mean_MKt.RF_portfolio, means_Portfolio)

 all_inputs_portfolio <- merge(first_portfolio, second_portfolio)

 second_pass_regression_portfolio <- lm(mu~ beta + Residuals, all_inputs_portfolio)

 summary(second_pass_regression_portfolio)
```

```{r}
 p_portfolio
```


## Exercise 2: Performance Evaluation I

Read Chapter 24 of our book. In this exercise use a Minimum Variance and a Tangecy (maxium Sharpe Ratio) portfolio calculate from your stocks, as well as the S&P500 as a benchmark (Period 2000-01-01 - 2020-01-11). For all three Investment Opportunities imagine you invest 100USD per month into the portfolio. What is the overall return this investment provides you? How much should you have investd at the bginning (one-time investment) to get the eact same overall wealth at the end of 2020? Can you plot both wealth developments over time?

First of all, I am going to download all the crucial data, which we need in order to create the three portfolios. My personal stock choice are Apple, Nividia, Microsoft, American Express, Walmart, Bank of America, Morgan Stanley, Disney, Exxon Mobile. As the benchmark we take the S&P500, regarding to the underling task. 
```{r Choosing my personal set of stocks}
install.packages("FinCal",dependencies=TRUE)
library(timetk, PortfolioAnalytics)
SP500 <- tq_index("SP500")
stock_names <- c("AAPL", "NVDA", "MSFT", "AXP", "WMT", "BAC", "GS","MS", "DIS", "XOM")
```

```{r Return Calculation of the stocks}
stock_returns <-tq_get(x = stock_names,get  = "stock.prices", from = "2000-01-01", to   = "2020-11-18") %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly")
unique(stock_returns$symbol)

stock_returns_xts <- stock_returns %>%
                      subset( select = c(symbol,date, monthly.returns)) %>% 
                      pivot_wider(names_from = symbol, 
                                  values_from = monthly.returns) %>% 
                      tk_xts(date_var = date, silent = TRUE)
stock_returns_xts
```
```{r Maximum Return}
port <- portfolio.spec(assets=stock_names)%>%
  add.constraint(type="full_investment") %>% #In our approach we use full_investment as our task it is
  add.constraint(type="long_only") %>% #We do not take any short-selling
  add.objective(type="return", name="mean")
port
```

Now I defined the investment constrains for our investments, which is based on our task a full investment constrained, due to the fact that we would like to be fully invested the whole period. The long only constrain comes from the idea that we are just caring about the upper part of the efficient frontier, in order to construct the tangency portfolio, which is the __most efficient__ way to set our 10 stocks together concerning the faced risked. So, in order words the tangency portfolio maximizes the Sharpe Ratio. The second portfolio we have to construct is the Minimum-Variance Portfolio. The _"third"_ portfolio would be an absolute passive strategy we would just invest in the SP500.

```{r }
opt_port <- optimize.portfolio(R=stock_returns_xts, portfolio=port,
                                 optimize_method="ROI", trace=TRUE)
opt_port
```
So, as we see we would make the most money if we would just everything in Nividia. However, we do not look for the maximum return, we look for the maximum return in relation to the taken risk.

```{r}
plot(opt_port, chart.assets=TRUE, main="Maximum Return", 
      xlim=c(0,0.3), ylim=c(0,0.5))
```
```{r Risk Reward trade of}
chart.RiskReward(opt_port,return.col="mean", risk.col="sd",
                 chart.assets=TRUE, 
                 xlim=c(0, 0.25),
                 main="Maximum Return")
```

```{r}
frontier <- create.EfficientFrontier(R=stock_returns_xts, 
                                       portfolio=port, 
                                       type="mean-StdDev")
chart.EfficientFrontier(frontier, match.col="StdDev", type="l",rf = 0, tangent.line = TRUE, chart.assets = TRUE,)
```
The efficient frontier is the set of optimal portfolios that offer the highest expected return for a defined level of risk or the lowest risk for a given level of expected return. Portfolios that lie below the efficient frontier are sub-optimal because they do not provide enough return for the level of risk.

```{r Tangency Portfolio}
port_tan <- portfolio.spec(assets=stock_names) %>%
  add.constraint(type="full_investment") %>%
  add.constraint(type = "long_only") %>%
  add.objective(type="return", name="mean")
port_tan
```

```{r Constructing the Tangency Portfolio}
init.portf <- portfolio.spec(assets=stock_names)
init.portf <- add.constraint(portfolio=init.portf, type="full_investment")
init.portf <- add.constraint(portfolio=init.portf, type="long_only")
init.portf <- add.objective(portfolio=init.portf, type="return", name="mean")
init.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")
init.portf
```
Maximizing the Sharpe Ratio can be formulated as a quadratic programming problem and solved very quickly using optimize_method="ROI". Although "StdDev" was specified as an objective, the quadratic programming problem uses the variance-covariance matrix in the objective function.

The default actin if "mean" and "StdDev" are specified as objectives with optimize_method="ROI" is to maximize quadratic utility. If we want to maximize Sharpe Ratio, we need to pass in maxSR=TRUE to optimize the portfolio, which should lead us to the tangency portfolio.

```{r }
maxSR.lo.ROI <- optimize.portfolio(R=stock_returns_xts, portfolio=init.portf, optimize_methode="ROI", maxSR=TRUE, trace=TRUE, search_size= 1000000)
maxSR.lo.ROI
```
It needed 17 Iterations to come as close as possible to the tangeny portfolio.
We would put a big stake into Walmart, Exxon-Mobil, Disney.
Although the maximum Sharpe Ratio objective can be solved quickly and accurately with optimize_method="ROI", it is also possible or DEoptim. These solvers have the added flexibility of using different methods to calculate the Sharpe Ratio (e.g. we could specify annualized measures of risk and return).

```{r Use random portfolios to run the optimization}
maxSR.lo.RP <- optimize.portfolio(R=stock_returns_xts, portfolio=init.portf, optimize_method="random", search_size=1000000, trace=TRUE)
maxSR.lo.RP
chart.RiskReward(maxSR.lo.RP, risk.col="StdDev", return.col="mean")
maxSR.lo.DE <- optimize.portfolio(R=stock_returns_xts, portfolio=init.portf, optimize_method="DEoptim", search_size=1000000, trace=TRUE)
maxSR.lo.DE
chart.RiskReward(maxSR.lo.DE, risk.col="StdDev", return.col="mean")
```

The Minmimum Variance Portfolio minimizing the variance of the portfolio

```{r Minimum Variance}
port_minvar <- portfolio.spec(assets=stock_names) %>%
  add.constraint(type="full_investment") %>%
  add.constraint(type = "long_only") %>%
  add.objective(type="risk", name="var")
port_minvar
```

```{r}
opt_minvar <- optimize.portfolio(R = stock_returns_xts,portfolio = port_minvar,
                              optimize_method = "ROI", trace = TRUE, search_size= 1000000)
opt_minvar
```

So, as we can see we see that we would invest the majority of our capital in Walmart and Exxon Mobile, due to the low standard deviation and quiet interesting we would not invest in Nividia.

```{r}
chart.RiskReward(opt_minvar,return.col="mean", risk.col="sd",
                 chart.assets=TRUE, 
                 xlim=c(0.01, 0.25),
                 main="Minimum Variance")
```

```{r Weight setting for the Portfolios}
wts_tan <- c(0.104, 0.002, 0.120, 0.002, 0.444, 0.006, 0.026, 0.000, 0.094, 0.202)
wts_minvar <- c(0.0241, 0.0000, 0.0615, 0.0000, 0.4551, 0.0000, 0.0000, 0.0000, 0.1094, 0.3499)# Weights from the Minimum Variance Portfolio
```

Portfolio Returns
```{r}
stock_returns

tan_port <- stock_returns %>%
    tq_portfolio(assets_col  = symbol, 
                 returns_col = monthly.returns, 
                 weights     = wts_tan, 
                 col_rename  = "Return")
tan_port
minvar_port <- stock_returns %>%
    tq_portfolio(assets_col  = symbol, 
                 returns_col = monthly.returns, 
                 weights     = wts_minvar, 
                 col_rename  = "Return")
minvar_port
```
For this task we take the SP500 as the benchmark.
```{r SP500}
bench_returns <- "^GSPC" %>%
    tq_get(get  = "stock.prices",
           from = "2000-01-01",
           to   = "2020-11-17") %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Benchmark")
bench_returns
```
```{r Merging the data together}
ov_tan_port <- left_join(tan_port,bench_returns,by = "date")
ov_minvar_port <- left_join(minvar_port,bench_returns,by = "date")

ov_tan_port
ov_minvar_port
```
```{r Overview for the Tangency Portfolio}
ov_tan_port %>%
    tq_performance(Ra = Return, Rb = Benchmark, 
    performance_fun = table.CAPM)
```

```{r Overview for the Minimum Variance}
ov_minvar_port %>%
    tq_performance(Ra = Return, Rb = Benchmark, 
    performance_fun = table.CAPM)
```

```{r Sharpe Ratio}
sharp_tan <- (0.1112-0)/sd(ov_tan_port$Return)
sharp_minvar <- (0.0605-0)/sd(ov_minvar_port$Return)
sharp_bench <- 1 #Checked it on the internet on an average base it is close to 1
ov_sharp <- cbind(sharp_tan, sharp_minvar, sharp_bench)
ov_sharp
```


```{r Mean calc}
tan_r <- geometric.mean(ov_tan_port$Return)
minvar_r <- geometric.mean(ov_minvar_port$Return)
bench_r <- geometric.mean(ov_minvar_port$Benchmark)
```

```{r Future Value}
FV_tan <- FV(rate = tan_r, nper = 1:239,pv = 100,pmt = -100,type = 1)
FV_minvar <- FV(rate = minvar_r, nper = 1:239, pv = 100, pmt = -100, type=1)
FV_bench <- FV(rate=bench_r, nper = 1:239, pv = 100, pmt = -100, type = 1)
id <- c(1:239)
as.data.frame(FV_tan)
as.data.frame(id)
as.data.frame(FV_minvar)
as.data.frame(FV_bench)
ov_list <- cbind(id, FV_tan,FV_minvar,FV_bench)
ov_list <- as.data.frame(ov_list)
invest <- ov_list[239,]
invest
```


```{r Ploting the wealth development over time}
ggplot(ov_list, aes(x=id)) + 
  geom_line(aes(y = FV_tan), color = "darkred") + 
  geom_line(aes(y = FV_minvar), color="steelblue", linetype="twodash") +
  geom_line(aes(y=FV_bench), color="pink") +
  ggtitle("Wealth Development")
```
The tangency portfolio performed very good related to the others.

```{r If we would want to get the same amount of money with a one time investment}
PV_tan <- PV(rate = tan_r,nper = 1:239,fv =-invest$FV_tan ,pmt = 0,type = 1)
PV_minvar <- PV(rate =minvar_r, nper=1:239,fv=-invest$FV_minvar,pmt=0,type=1)
PV_bench <- PV(rate=bench_r, nper=1:239, fv=-invest$FV_bench,pmt=0, type=1)
id <- c(1:239)
as.data.frame(PV_tan)
as.data.frame(id)
as.data.frame(PV_minvar)
as.data.frame(PV_bench)
ov_list <- cbind(id, PV_tan,PV_minvar,PV_bench)
ov_list <- as.data.frame(ov_list)
invest <- ov_list[239,]
invest
```
As we see if we would like to make an one time investment, but also the exact same amount of money as if we would invest 100 dollar per month. For the Tangency Portfolio we would need to invest __3109.721__$ for the Minimium Variance Portfolio we would need to invest __4695.70__$ and for the passive strategy S&P500 we would need __5082.05__. So, as we can see that the intrest of intrest effect takes here part and dramatically increases the future value of our investment.

```{r}
ggplot(ov_list, aes(x=id)) + 
  geom_line(aes(y = PV_tan), color = "darkred") + 
  geom_line(aes(y = PV_minvar), color="steelblue", linetype="twodash") +
  geom_line(aes(y=PV_bench), color="pink") +
  ggtitle("One time investment")
```



## Exercise 3: Performance Evaluation II

For the same two portfolios and the appropriate benchmark calculate overall performance measures (Sharpe ratio, M2 [assume a risk-fre rate of 0], Treynor Ratio, Jensen's Alpha and Information ratio). Interpret. Additional do the two market timing regressions (ch 24.4) and see whether your portfolios can "time" the market.


## Exercise 4: Active Portfolio Management

Work through trough the demo `demo(relative_ranking)`. Use what you lear here, form an appropriate opinion on the ranking of your assets and optimize a Minimum Variance and Maximum Sharpe ratio Portfolio. Which one performs better?

demo(relative_ranking)

data(edhec)
```{r}
library(PortfolioAnalytics)
data(edhec)
R <- edhec[,1:4]
```

```{r}
funds <- colnames(R)
```
 #' Construct initial portfolio with basic constraints.
```{r}
init.portf <- portfolio.spec(assets=funds)
```

```{r}
init.portf <- add.constraint(portfolio=init.portf, type="weight_sum", 
                              min_sum=0.99, max_sum=1.01)
```

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
init.portf <- add.constraint(portfolio=init.portf, type="box",min=0.05, max=0.5)
```

```{r}
init.portf <- add.objective(portfolio=init.portf, type="risk", name="StdDev")
```

```{r}

init.portf <- add.objective(portfolio=init.portf, type="return", name="mean")

init.portf
```
> #' Here we express views on the relative rank of the asset returns.

```{r}
asset.rank <- c(2, 3, 1, 4)

```


> #' Use Meucci Fully Flexible Views framework to express views on the relative
> #' order of asset returns.
> #' Define prior probabilities.

```{r}
p <- rep(1 / nrow(R), nrow(R))
```

> #' Express view on the relative ordering of asset returns

```{r}
m.moments <- meucci.ranking(R, p, asset.rank)

```


 #' Express views using the method described in Almgren and Chriss, 
> #' "Portfolios from Sorts".

```{r}
 ac.moments <- list()

ac.moments
```

```{r}
ac.moments$mu <- ac.ranking(R, asset.rank)
```

> # Sample estimate for second moment

```{r}
ac.moments$sigma <- cov(R)
```

> #' Generate random portfolios for use in the optimization.

```{r}
rp <- random_portfolios(init.portf, 5000)

rp
```
> #' Run the optimization using first and second moments estimated from 
> #' Meucci's Fully Flexible Views framework using the moments we calculated


```{r}
opt.meucci <- optimize.portfolio(R, 
                                 init.portf,
                                 optimize_method="random", 
                                 rp=rp, 
                                 trace=TRUE,
                                 momentargs=m.moments)

opt.meucci


```

> #' Run the optimization using first moment estimated based on Almgren and Chriss, 
> #' "Portfolios from Sorts". The second moment uses the sample estimate.

```{r}
opt.ac <- optimize.portfolio(R,
                             init.portf,
                             optimize_method="random",
                             rp=rp,
                             trace=TRUE,
                             momentargs=ac.moments)

opt.ac

```
> #' For comparison, run the optimization using sample estimates for first and 
> #' second moments.

```{r}
opt.sample <- optimize.portfolio(R, 
                                 init.portf, 
                                 optimize_method="random", 
                                 rp=rp,
                                 trace=TRUE)
opt.sample

```


> #' Here we plot the optimal weights of each optimization.

```{r}
chart.Weights(combine.optimizations(list(meucci=opt.meucci, 
                                           ac=opt.ac,
                                           sample=opt.sample)), 
                
              
ylim=c(0,1), plot.type="barplot")

```
> #' Here we define a custom moment function to estimate moments based on 
> #' relative ranking views.
> #' Asset are ranked according to a momentum or reversal view based on the 
> #' previous n periods.

```{r}
moment.ranking <- function(R, n=1, momentum=TRUE, method=c("meucci", "ac")){
  
# Moment function to estimate moments based on relative ranking of 
# expected returns.
  
method <- match.arg(method)
   
   # Use the most recent n periods of returns
   tmpR <- apply(tail(R, n), 2, function(x) prod(1 + x) - 1)
   
   if(momentum){
     # Assume that the assets with the highest return will continue to outperform
     asset.rank <- order(tmpR)
   } else {
# Assume that the assets with the highest return will reverse
asset.rank <- rev(order(tmpR))
}
switch(method,
meucci = {
            # Meucci Fully Flexible Views framework
            # Prior probabilities
           p <- rep(1 / nrow(R), nrow(R))
            
            # Relative ordering view
           moments <- meucci.ranking(R, p, asset.rank)
          },
          ac = {
            # Almgren and Chriss Portfolios from Sorts
            moments <- list()
            moments$mu <- ac.ranking(R, asset.rank)
            # Sample estimate for second moment
            moments$sigma <- cov(R)
         }
   )
   return(moments)
 }


```

> #' Here we run out of sample backtests to test the out of sample performance using the different frameworks to express our views on relative asset return ranking.

```{r}
opt.bt.meucci <- optimize.portfolio.rebalancing(R, init.portf, 
                                               optimize_method="random",  
                                               rebalance_on="quarters", 
                                               training_period=100,
                                               rp=rp,
                                               momentFUN="moment.ranking",
                                               n=2,
                                               momentum=TRUE,
                                               method="meucci")

```
```{r}
opt.bt.ac <- optimize.portfolio.rebalancing(R, init.portf, 
optimize_method="random", 
rebalance_on="quarters", 
rp=rp,
momentFUN="moment.ranking",
n=2,
momentum=TRUE,
method="ac")
```

```{r}

opt.bt.sample <- optimize.portfolio.rebalancing(R, init.portf, 
                                                 optimize_method="random", 
                                                 rebalance_on="quarters", 
                                                 training_period=100,
                                                 rp=rp)
```

> #' Compute returns and chart performance summary.

```{r}
ret.meucci <- Return.portfolio(R, extractWeights(opt.bt.meucci))
```

```{r}
ret.ac <- Return.portfolio(R, extractWeights(opt.bt.ac))
```

```{r}
ret.sample <- Return.portfolio(R, extractWeights(opt.bt.sample))
```

```{r}
ret <- cbind(ret.meucci, ret.ac, ret.sample)
```

```{r}
colnames(ret) <- c("meucci.rank", "ac.rank", "sample")
```

```{r}
charts.PerformanceSummary(ret, main="Ranking Views Performance")
```



